Scalable predictive coding method and apparatus

ABSTRACT

A scalable predictive coder in which the current frame of data is predicted at the enhancement-layer by processing and combining the reconstructed signal at: (i) the current base-layer (or lower layers) frame; and (ii) the previous enhancement-layer frame. The combining rule takes into account the compressed prediction error of the base-layer, and the parameters used for its compression.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.10/816,733, filed on Apr. 2, 2004, now U.S. Pat. No. ______,incorporated herein by reference in its entirety, which is acontinuation of U.S. application Ser. No. 09/216,096, filed on Dec. 18,1998, now U.S. Pat. No. 6,731,811, incorporated herein by reference inits entirety, which claims priority to U.S. provisional application Ser.No. 60/068,331 filed on Dec. 19, 1997, incorporated herein by referencein its entirety.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not Applicable

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

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NOTICE OF MATERIAL SUBJECT TO COPYRIGHT PROTECTION

A portion of the material in this patent document is subject tocopyright protection under the copyright laws of the United States andof other countries. The owner of the copyright rights has no objectionto the facsimile reproduction by anyone of the patent document or thepatent disclosure, as it appears in the United States Patent andTrademark Office publicly available file or records, but otherwisereserves all copyright rights whatsoever. The copyright owner does nothereby waive any of its rights to have this patent document maintainedin secrecy, including without limitation its rights pursuant to 37 C. F.R. § 1.14.

REFERENCED PUBLICATIONS

The following publications which are referenced herein using numbers insquare brackets (e.g., [1]) are incorporated herein by reference:

-   [1] D. Wilson and M. Ghanbari, “Transmission of SNR scalable two    layer MPEG-2 coded video through ATM networks,” Proc. 7th    International Workshop on Packet Video, pp. 185-189, Brisbane    Australia, March 1996.-   [2] B. Girod, U. Horn, and B. Belzer, “Scalable video coding with    multiscale motion compensation and unequal error protection,” In Y.    Wang, S. Panwar, S.-P. Kim, and H. L. Bertoni, editors, Multimedia    Communications and Video Coding, pp. 475-482, New York: Plenum    Press, 1996.-   [3] B. G. Haskell, A. Puri, and A. N. Netravali, Digital video: an    introduction to MPEG-2. New York: Chapman and Hall, International    Thomson Pub., 1997.-   [4] Draft text of H.263, Version 2 (H.263+).-   [5] T. K. Tan, K. K. Pang, and K. N. Ngan, “A frequency scalable    coding scheme employing pyramid and subband techniques,” IEEE    Transactions on Circuits and Systems for Video Technology, pp.    203-207, April 1994.-   [6] A. Gersho and R. M. Gray, Vector Quantization and Signal    Compression. Kluwer Academic Press, 1992.

BACKGROUND OF THE INVENTION

1. Field of the Invention

This invention pertains generally to data compression methods andsystems, and more particularly to an efficient scalable predictivecoding method and system where most or all of the information availableto the enhancement-layer is exploited to improve the quality of theprediction.

2. Description of the Background Art

Many applications require data, such as video, to be simultaneouslydecodable at a variety of rates. Examples include applications involvingbroadcast over differing channels, multicast in a complex network wherethe channels/links dictate the feasible bit rate for each user, theco-existence of receivers of different complexity (and cost), andtime-varying channels. An associated compression technique is “scalable”if it offers a variety of decoding rates using the same basic algorithm,and where the lower rate information streams are embedded in the higherrate bit-streams in a manner that minimizes redundancy.

A predictive coding system for encoding and decoding a signal withoutscalability is well-known in the literature of signal compression. (Seefor example: predictive vector quantization [6], and motion-compensatedpredictive transform coding of video [3]). In such predictive codingsystems the encoder includes a decoder and memory so that what isactually encoded is the difference between the input signal and apredicted version of the reproduced signal, this difference signal beingcalled the residual. The decoder contains a prediction loop whereby thecurrent residual frame is decoded and then it is added to a predictionof the current frame obtained from the previous reproduced frame. Insome cases, the predictor uses several prior frames to predict thecurrent frame.

A major difficulty encountered in scalable predictive coding is how totake advantage of the additional information, available to theenhancement-layer decoder for improved prediction, without causingundesired conflicts with the information obtained from the base layer.FIG. 1 depicts a two-layer scalable coding system 10 where it is assumedthat the original input signal (e.g., an audio or video signal) issegmented into frames that are sequentially encoded. Typical examplesare video frames, and speech frames, but “frame” here will also coverthe degenerate case of a single sample as in differential pulse codedmodulation (DPCM). The term “frame” as used herein refers either to agroup of contiguous samples of an original input signal or a set ofparameters extracted from the original group of samples (such as a setof transform coefficients obtained by a discrete-cosine transform (DCT)operation on the original group of samples) and in each case theterminology “frame” or “signal” will be used to refer to this entitythat is representative of the original group of samples or is itself theoriginal group of samples.

The input frame 12, x(n), is compressed by the base encoder (BE) 14which produces the base bit-stream 16. The enhancement-layer encoder(EE) 18 has access to the input frame 12 and to any information producedby or available to BE 14. EE 18 uses this data to generate theenhancement-layer bit-stream 20. A base decoder (BD) 22 receives thebase bit-stream 16 and produces a reconstruction 24, {circumflex over(x)}_(b)(n), while the enhancement-layer decoder (ED) 26 has access toboth bit-streams and produces an enhanced reconstruction 28, {circumflexover (x)}_(e)(n). The reconstruction frames that are available at thedecoder are used to predict or estimate the current frame. Note that ED26 has access to both bit streams and hence it effectively has access toboth the reconstruction frame at the base layer, {circumflex over(x)}_(b)(n), and the previous reconstructed frame at the enhancementlayer {circumflex over (x)}_(e)(n−1), while BD 22 has only access to theprevious reconstructed frame at the base layer, {circumflex over(x)}_(b)(n−1), which is stored in the memory within BD. In the case of ascalable coding system with multiple enhancement layers, an enhancementlayer decoder may have access to the reconstruction frames from lowerenhancement layers as well as from the base layer. The prediction loop(internal to the operation of BD as in any predictive coding system butnot shown in the figure) in this configuration causes severedifficulties in the design of scalable coding. Accordingly, a number ofapproaches to scalable coding have been developed. These include,

-   -   (1) The standard approach: At the base layer, BE 14 compresses        the residual        r _(b)(n)=x(n)−P[{circumflex over (x)} _(b)(n−1)],        where P denotes the predictor (e.g., motion compensator in the        case of video coding). Note that for notational simplicity we        assume first-order prediction, but in general several previous        frames may be used. BD 22 produces the reconstruction        {circumflex over (x)} _(b)(n)=P[{circumflex over (x)}        _(b)(n−1)]+{circumflex over (r)} _(b)(n),        where {circumflex over (r)}_(b)(n) is the        compressed-reconstructed residual. At the enhancement-layer, EE        18 compresses the base layer's reconstruction error        r _(e) ⁽¹⁾ =x(n)−{circumflex over (x)}_(b)(n)=x(n)−P[{circumflex        over (x)} _(b)(n−1)]−{circumflex over (r)}_(b)(n).

The enhancement-layer reconstruction is{circumflex over (x)} _(e)(n)={circumflex over (x)} _(b)(n)+{circumflexover (r)}_(e) ⁽¹⁾(n)=P[{circumflex over (x)} _(b)(n−1)]+{circumflex over(r)}_(b)(n)+{circumflex over (r)}_(e) ⁽¹⁾(n).See, e.g., [1]. A deficiency of this approach is that no advantage istaken of the potentially superior prediction due to the availability of{circumflex over (x)}_(e)(n−1) at the ED 26.

(2) The separate coding approach: BE 14 compresses r_(b)(n) as above,but EE 18 compresses the “enhancement-only” prediction errorr _(e) ⁽2)=x(n)−P[{circumflex over (x)} _(e)(n−1)]directly. The enhancement-layer reconstruction is{circumflex over (x)} _(e)(n)=P[{circumflex over (x)}_(e)(n−1)]+{circumflex over (r)}_(e) ⁽²⁾(n).A deficiency of this approach is that, while the approach takesadvantage of information available only to the enhancement-layer, itdoes not exploit the knowledge of {circumflex over (r)}_(b)(n) which isalso available at the enhancement-layer. The two layers are, in fact,separately encoded except for savings on overhead information whichneeds not be repeated (such as motion vectors in video coding) [2].

(3) Layer-specific prediction at the decoder approach: BD 22reconstructs the frame as{circumflex over (x)} _(b)(n)=P[{circumflex over (x)}_(b)(n−1)]+{circumflex over (r)} _(b)(n),and ED 26 reconstructs as{circumflex over (x)} _(e)(n)=P[{circumflex over (x)}_(e)(n−1)]+{circumflex over (r)}_(b)(n)+{circumflex over (r)}_(e)(n)However, the encoders BE 14 and EE 18 use the same prediction [3], andthe options are:

-   -   (a) Both encoders use base-layer prediction P[{circumflex over        (x)}_(b)(n−1)]. This results in drift of the enhancement-layer        decoder. (The term “drift” refers to a form of mismatch where        the decoder uses a different prediction than the one assumed by        the encoder. This mismatch tends to grow as the “corrections”        provided by the encoder are misguiding, hence, the decoder        “drifts away”).

(b) Both encoders use enhancement-layer prediction P[{circumflex over(x)}_(e)(n−1)]. This results in drift of the base-layer decoder.

(4) Switch between approaches (1) and (2) on a per frame or per blockbasis [4], or per sample [5]. This approach has the deficiencies ofeither approach (1) or (2) as described above, at each time depending onthe switching decision.

BRIEF SUMMARY OF THE INVENTION

The present invention addresses the prediction loop deficiencies inconventional scalable coding methods and systems in a way that achievesefficient scalability of predictive coding. The approach is generallyapplicable and may, in particular, be applied to standard video andaudio compression. In the present invention, most or all of theinformation available at an enhancement-layer may be exploited toimprove the quality of the prediction.

By way of example, and not of limitation, in the present invention thecurrent frame is predicted at the enhancement-layer by processing andcombining the reconstructed signal representing: (i) the currentbase-layer (or lower layers) frame; and (ii) the previousenhancement-layer frame. The combining rule takes into account thecompressed prediction error of the base-layer, and the parameters usedfor its compression. The main difficulty overcome by this invention isin the apparent conflicts between these two sources of information andtheir impact as described in the Background of the Invention. Thisdifficulty may explain why existing known methods exclusively use one ofthese information sources at any given time. These methods will begenerally referred to here as switching techniques (which include as aspecial case the exclusive use of one of the information sources at alltimes). Additionally, the invention optionally includes a specialenhancement-layer synchronization mode for the case where thecommunication rate for a given receiver is time varying (e.g., in mobilecommunications). This mode may be applied periodically to allow thereceiver to upgrade to enhancement-layer performance even though it doesnot have prior enhancement-layer reconstructed frames.

An object of the invention is to achieve efficient scalability ofpredictive coding.

Another object of the invention is to provide a method and system forscalable predictive coding that is applicable to typical or standardvideo and audio compression.

Another object of the invention is to provide a scalable predictivecoding method and system in which all or most of the informationavailable at an enhancement-layer is exploited to improve the quality ofthe prediction.

Further objects and advantages of the invention will be brought out inthe following portions of the specification, wherein the detaileddescription is for the purpose of fully disclosing preferred embodimentsof the invention without placing limitations thereon.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention will be more fully understood by reference to thefollowing drawings which are for illustrative purposes only:

FIG. 1 is functional block diagram of a conventional two-layer scalablepredicting coding system.

FIG. 2 is a functional block diagram of an enhancement layer encoder ofa scalable predictive coding system in accordance with the presentinvention.

FIG. 3 is a functional block diagram of a base layer reconstructionmodule according to the present invention.

FIG. 4 is a functional block diagram of an enhancement layerreconstruction module according to the present invention.

FIG. 5 is a functional block diagram of a three-layer scalable encoderemploying the enhancement encoder of the present invention.

FIG. 6 is a functional block diagram of a three-layer scalable decodercorresponding to the encoder shown in FIG. 5.

FIG. 7 is a functional block diagram of a two-layer scalable videoencoder employing the enhancement encoder of the present invention.

FIG. 8 is a functional block diagram of a two-layer decodercorresponding to the encoder shown in FIG. 7.

FIG. 9 is a functional block diagram of the spatial motion compensatorblocks shown in FIG. 7 and FIG. 8.

DETAILED DESCRIPTION OF THE INVENTION

Referring more specifically to the drawings, where like referencenumbers, labels and symbols denote like parts, for illustrative purposesthe present invention will be described with reference to the encodergenerally shown in FIG. 2, as well as the encoding system shown in FIG.2 through FIG. 6, and the scalable predictive coding method described inconnection therewith. Various embodiments of encoders and decodersemploying the present invention, and details therefore, are shown anddescribed in FIG. 7 through FIG. 9.

The method of the present invention generally comprises upgrading theprediction used at each enhancement-layer by combining, with minimalconflict, the information provided from both sources, namely,information available at, and used by, the base-layer (or lower layers),and information that is available only at the enhancement-layer. In thecase of a scalable predictive coding system with multiple enhancementlayers, the prediction at an enhancement layer may combine informationprovided from all lower enhancement layers as well. The inventionprovides for prediction or estimation of the signal frame itself in anyrepresentation, or any subset of signal representation coefficients suchas transform coefficients (e.g., in video, audio), line spectralfrequencies (e.g., in speech or audio), etc. The term “frame” and thecorresponding mathematical notation will be used generally to refer tothe relevant set of frame coefficients being estimated or predicted bythe method in each particular application.

Referring first to FIG. 2, a functional block diagram of an enhancementlayer encoder of a scalable predictive coding system in accordance withthe present invention is shown. In the enhancement layer encoder 100 ofthe present invention, an enhancement layer estimator (ELE) 102 computesa new predicted frame 104, {tilde over (x)}_(e)(n), by combininginformation from the reconstruction frame 106 at the base layer,{circumflex over (x)}_(b)(n) and from the previous reconstructed frame108 at the enhancement layer {circumflex over (x)}_(e)(n−1). Note thatfirst order prediction is described for notational simplicity butseveral previous frames may be used. The combining rule depends on anyor all of, but not limited to, the following parameters: the compressionparameters 110 of the base layer (such as quantization step andthreshold, and the quantized base-layer residual 112, {circumflex over(r)}_(b)(n), (see FIG. 3)), and the statistical parameters 114 of thetime evolution of the frames (such as inter-frame correlationcoefficients and variance). The statistical parameters may be eitherestimated off-line from training data, or estimated on-line by anadaptive estimator which tracks variation in the signal statistics basedon either the original signal (in which case the parameters need to betransmitted to the decoder) or based on reconstructed signals which areavailable to the receiver. The exact definition of the combination ruledepends on the level of complexity allowed for the module. At the highend, one may compute a possibly complex, optimal predicted frame givenall the available information. The enhancement layer residual 116,r_(e)(n), which is the difference between the input frame 118, x(n), andthe predicted frame 104, {tilde over (x)}_(e)(n), is then compressed bya compressor 120 to produce the enhancement bits 122.

Referring to FIG. 3 through FIG. 6, a complete scalable predictivecoding system for use with this invention is shown. While only threelayers are shown, it will be appreciated that additional layers can beadded and are contemplated within the scope of the invention. FIG. 3shows a base layer reconstruction module 124 which receives thequantized base layer residual 112, {circumflex over (r)}_(b)(n), andadds it to the base predicted frame 126, {tilde over (x)}_(b)(n), toproduce the base layer reconstruction frame 106, {circumflex over(x)}_(b)(n). A delay 128 produces a delayed base reconstructed frame130, {circumflex over (x)}_(b)(n−1), which is input to the basepredictor 132 which computes the base predicted frame 126, {tilde over(x)}_(b)(n), which is needed to produce the reconstructed frame asexplained above.

The enhancement layer reconstruction module 134 shown in FIG. 4 receivesthe quantized enhancement layer residual 136, {circumflex over(r)}_(e)(n), and adds it to the enhancement layer predicted frame 104,{tilde over (x)}_(e)(n), to produce the enhancement layer reconstructionframe 138, {circumflex over (x)}_(e)(n). A delay 140 produces a delayedenhancement layer reconstructed frame 108, {circumflex over(x)}_(e)(n−1), which is input to the enhancement layer estimator 102,which in turn computes the enhancement layer predicted frame 104, {tildeover (x)}_(e)(n), as explained with reference to FIG. 2.

FIG. 5 shows how the modules described in FIG. 2 through FIG. 4 may becombined to obtain a complete scalable predictive encoder. Only threelayers are shown without implying any limitation, as extensions tofurther layers is obvious and straightforward. Most inputs and outputswere explained in the context of the previous figures, and todistinguish between the notation for the first and second enhancementlayer signals, the prefix EL1 or EL2 was added, respectively.

The signal frame to be compressed (which may be the original raw signal,or any set of coefficients extracted from it for the purpose ofcompression) denoted x(n) is fed to all layers in parallel. In eachlayer the predicted frame ({tilde over (x)}_(b)(n) in the base layer,(EL1){tilde over (x)}_(e)(n) in the first enhancement layer, and (EL2){tilde over (x)}_(e)(n) at the second enhancement layer) is subtractedfrom x(n) to obtain the prediction error (or residual) at the layer(r_(b)(n), (EL1) r_(e)(n), and (EL2) r_(e)(n), for the base, firstenhancement and second enhancement layers, respectively). The residualis compressed by the layer's Compressor/Quantizer which outputs: thelayer's bits for transmission to the decoder, the reconstructed(quantized) residual ({circumflex over (r)}_(b)(n), (EL1) {circumflexover (r)}_(e)(n), and (EL2) {circumflex over (r)}_(e)(n), for the base,first enhancement and second enhancement layers, respectively), as inputto the layer's reconstruction module, and the set of compressionparameters for use by a higher layer. Note that the enhancement layercompressor/quantizer subsumes the compressor 120 of FIG. 2 as, besidethe bit stream, it also outputs the quantized residual. Thereconstruction module of each layer processes its input signals as perFIGS. 3 and 4, and outputs the reconstructed frame for the layer({circumflex over (x)}_(b)(n), (EL1) {circumflex over (x)}_(e)(n), and(EL2) {circumflex over (x)}_(e)(n), for the base, first enhancement andsecond enhancement layers, respectively), and the layer's predictedframe ({tilde over (x)}_(b)(n), (EL1) {tilde over (x)}_(e)(n), and (EL2){tilde over (x)}_(e)(n), for the base, first enhancement and secondenhancement layers, respectively).

The corresponding three layer scalable predictive decoder is shown inFIG. 6. Each layer's inverse compressor/quantizer receives as input thelayer's bit stream from which it reproduces the layer's quantizedresidual. It also extracts the layer's compression parameters for use bya higher layer reconstruction module. The rest of the diagram isidentical to the encoder of FIG. 2 and similarly produces thereconstructed frame at each layer.

It will be appreciated that the invention is generally applicable topredictive coding and, in particular, may be applied to known vectorquantizer-based compression techniques, and known transform-basedtechniques. Further, it is applicable to compression of speech, audio,and video signals. A combining rule employing optimal estimation forscalable compression is described next as an implementation example ofthe invention.

In typical predictive coding, a number of signal representationcoefficients (e.g., vectors of transform coefficients, line spectralfrequencies, or vectors of raw signal samples) are extracted per frameand quantized independently. A specific low complexity implementation ofthe invention consists of optimally combining the information availablefor predicting the coefficient at an enhancement-layer. Thereconstructed coefficient at the base-layer, {circumflex over(x)}_(b)(n), and the quantization interval (or partition region in thecase of vector quantization) of the corresponding reconstructed residual{circumflex over (r)}_(b)(n), determine an interval/cell I(n) withinwhich the original coefficient x(n) must lie. From the correspondingreconstructed coefficient at the previous enhancement-layer frame,{circumflex over (x)}_(e)(n−1), and a statistical model on timeevolution of the coefficients, one may construct a probability densityfunction for x(n) conditional on {circumflex over (x)}_(e)(n−1), denotedby p[x(n)|{circumflex over (x)}_(e)(n−1)]. The optimal estimate of x(n)is obtained by expectation:${{\overset{\sim}{x}}_{e}(n)} = {\frac{\int_{I{(n)}}{{{xp}\left\lbrack {{x(n)}{{\hat{x}}_{e}\left( {n - 1} \right)}} \right\rbrack}{\mathbb{d}x}}}{\int_{I{(n)}}{{p\left\lbrack {{x(n)}❘{{\hat{x}}_{e}\left( {n - 1} \right)}} \right\rbrack}{\mathbb{d}x}}}.}$This predictor incorporates the information provided by the base-layer(interval within which x(n) lies), and by the enhancement-layer(probability distribution of x(n) conditional on {circumflex over(x)}_(e)(n−1)).

Referring now to FIG. 7 and FIG. 8, a system for scalable predictivetransform coding which is designed for the compression of video signalsis shown. In current practice and standards (e.g., [4]), the system usesmotion compensation for basic frame prediction, applies the discretecosine transform (DCT) to the prediction error (residual), and quantizesthe transform coefficients one at a time. A block diagram of a two-layerscalable video encoder is shown in FIG. 7, and the corresponding decoderis shown in FIG. 8. FIG. 9 shows a functional block diagramcorresponding to the spatial motion compensator blocks shown in the baselayer and the enhancement later.

Note that, for simplicity, the symbols x,r, {circumflex over (x)},{circumflex over (r)},{tilde over (x)} for the video and residualsignals at the base and enhancement layers in the diagram are in thetransform domain, even though motion compensation is performed in thespatial domain (FIG. 9). Note further that additional enhancement layersmay be added where an enhancement layer k builds on and relates to layerk−1 below it exactly as shown for the first two enhancement layers.

The first-order Laplace-Markov process was chosen for modeling the timeevolution statistics of the video signal:x(n)=ρMC[x(n−1)]+z(n),where x(n) is the DCT coefficient in the current frame and MC[x(n−1)] isthe corresponding (after motion compensation) coefficient in theprevious frame. The correlation coefficient ρ is assumed to be nearlyone. As x(n) has a Laplacian density, the driving process, z(n), iszero-mean, white, stationary, and has the densityp(z)=ρ²δ(z)+(1−ρ²)α/2e ^(−α|z|.)(Both α and ρ may in practice be estimated “offline” from training data,or via an adaptive estimator that tracks variations in local statisticsof the signal). The base layer performs standard video compression: itspredictor consists only of motion compensation, {circumflex over(x)}_(b)(n)=MC[{circumflex over (x)}_(b)(n−1)], the residualr_(b)(n)=x(n)−{tilde over (x)}_(b)(n) is quantized and the correspondingindex is transmitted. Let [a,b] be the quantization interval, hencer_(b)(n)ε[a,b]. Thus the information the base layer provides on x(n) iscaptured in the statement:x(n)ε[{tilde over (x)} _(b)(n)+a,{tilde over (x)} _(b)(n)+b].

At the enhancement layer, the prediction exploits the informationavailable from both layers. The optimal predictor is given therefore bythe expectation:{tilde over (x)} _(e)(n)=E{x(n)|{circumflex over (x)}_(e)(n−1),x(n)ε[{tilde over (x)} _(b)(n)+a,{tilde over (x)} _(b)(n)+b]},which is conveniently rewritten as{tilde over (x)} _(e)(n)={overscore (x)} _(e)(n−1)+E{z(n)|z(n)εI_(z)(n)}where{tilde over (x)}_(e)(n−1)=MC[{circumflex over (x)} _(e)(n−1)]and the expectation interval isI _(z)(n)=[{tilde over (x)} _(b)(n)+a−{tilde over (x)} _(e)(n−1 ),{tilde over (x)} _(b)(n)+b−{tilde over (x)} _(e)(n−1)]

This prediction is directly implemented using the model for p(z) givenabove:${{\overset{\sim}{x}}_{e}(n)} = {{{\overset{\_}{x}}_{e}\left( {n - 1} \right)} + {\frac{\int_{I_{z}{(n)}}{{{zp}(z)}{\mathbb{d}z}}}{\int_{I_{z}{(n)}}{p(z){\mathbb{d}z}}}.}}$

The integral may be analytically evaluated and its closed form solutiongiven explicitly in terms of the integral limits and the parameters α,β, is normally used for simple implementation.

This embodiment of the invention is of low complexity, uses standardvideo compression for its base layer, and provides substantialperformance gains which build up and increase with the number of layersimplemented. Its absence in all leading standards in spite of its gainsand low complexity strongly suggests that the invention is not obviousto the leading researchers and developers in the field of videocompression.

The scalable predictive coding method of the invention, althoughillustrated herein on a two or three-layer scalable system, isrepeatedly applicable to further layers of enhancement in astraightforward manner. For example, at layer k we combine signalinformation from the current reconstructed frame at layer k−1, and fromthe previous reconstruction frame at layer k. A higher complexityversion allows for the combining rule to take into account data from alllower layers. In the special implementation described, information fromall lower layers contributes to restricting the final interval withinwhich the coefficient must lie. Another higher complexity version useshigher order prediction (based on multiple past frames).

Another application of the invention pertains to time-varying channels,such as mobile communications, and most common network communications.When the receiver experiences an improvement in channel conditions, itattempts to decode higher enhancement bits and improve the quality ofthe reconstruction. However, it can not compute the enhancement layerprediction as past enhancement layer reconstruction frames were notdecoded and are not available. The present invention includes a solutionto this problem, which comprises periodically (e.g., once per fixednumber of frames) constraining the enhancement encoder to exclusivelyuse lower layer information for the prediction. This periodicconstrained prediction synchronizes the enhancement decoder with theenhancement encoder and allows the receiver to decode theenhancement-layer signals. The frequency of application of thisconstrained mode may be different for each layer and may be optimizedfor the time-varying channel statistics. The trade off is between sometemporary degradation in prediction (when the prediction is constrained)and the receiver's capability to upgrade to enhancement layerperformance as the channel conditions improve.

Finally, it will be appreciated that the scalability advantages of theinvention may be easily combined with known methods for temporal andspatial scalability.

Accordingly, it will be seen that this invention provides for efficientscalability of predictive coding that is applicable to standard videoand audio compression. The invention uses most or all of the informationavailable at an enhancement-layer to improve the quality of theprediction. In addition, the invention provides for enhancement-layersynchronization to accommodate situations where the communication ratefor a given receiver is time varying (e.g., in mobile communications).Although the description above contains many specificities, these shouldnot be construed as limiting the scope of the invention but as merelyproviding illustrations of some of the presently preferred embodimentsof this invention. Thus the scope of this invention should be determinedby the appended claims and their legal equivalents.

1. A method for predicting the current frame of data in a digital codingsystem wherein a signal is segmented into frames of data that aresequentially encoded, said system including a base layer and anenhancement layer, said base layer including a base encoder and a basedecoder, said enhancement layer including an enhancement encoder, anenhancement layer estimator, and an enhancement decoder, said basedecoder producing a reconstructed signal, said enhancement layerestimator producing a prediction of said current frame of data, saidenhancement decoder producing an enhanced reconstructed signal, saidmethod comprising: applying the reconstructed data representing thecurrent base layer frame and the reconstructed data representing theprevious enhancement layer frame as inputs to the enhancement layerestimator; applying additional information from the base layer asadditional input to the enhancement layer estimator; and predicting thecurrent frame of data at the enhancement layer by processing andcombining said inputs. 2-14. (canceled)
 15. A method for predicting thecurrent frame of data in a digital coding system wherein a signal issegmented into frames of data that are sequentially encoded, said systemincluding a base layer and an enhancement layer, said base layerincluding a base encoder and a base decoder, said enhancement layerincluding an enhancement encoder, an enhancement layer estimator, and anenhancement decoder, said base decoder producing a reconstructed signal,said enhancement layer estimator producing a prediction of said currentframe of data, said enhancement decoder producing an enhancedreconstructed signal, said method comprising: applying the reconstructeddata representing the current base layer frame and the reconstructeddata representing the previous enhancement layer frame as inputs to theenhancement layer estimator; applying values of compression parametersemployed by the base layer encoder as additional inputs to theenhancement layer estimator; and predicting the current frame of data atthe enhancement layer by processing and combining said inputs.
 16. Themethod of claim 15, wherein said compression parameters are parametersused for compressing the base layer prediction error.
 17. The method ofclaim 15, wherein said compression parameters include a quantizationinterval employed in the base layer compressor.
 18. A method forpredicting the current frame of data in a digital coding system whereina signal is segmented into frames of data that are sequentially encoded,said system including a base layer and an enhancement layer, said baselayer including a base encoder and a base decoder, said enhancementlayer including an enhancement encoder, an enhancement layer estimator,and an enhancement decoder, said base decoder producing a reconstructedsignal, said enhancement layer estimator producing a prediction of saidcurrent frame of data, said enhancement decoder producing an enhancedreconstructed signal, said method comprising: applying the reconstructeddata representing the current base layer frame and the reconstructeddata representing the previous enhancement layer frame as inputs to theenhancement layer estimator; applying a set of statistical parameters ofthe time evolution of the signal frames as inputs to the enhancementlayer estimator; and predicting the current frame of data at theenhancement layer by processing and combining said inputs.
 19. A methodfor scalable predictive coding of a signal, comprising: encoding datarepresenting said signal with a base layer predictive coding system thatprovides a first prediction of said signal and information indicative ofa decoded base layer approximation to said signal; and encoding datarepresenting said signal by a first enhancement layer which performspredictive coding with a second prediction of said signal derived from acombination of information from the current base layer and informationfrom the past decoded approximation to said signal generated in saidfirst enhancement layer; wherein said coding system includes a secondenhancement layer and wherein said second enhancement layer performspredictive coding with a third prediction of said signal derived from acombination of information from said first enhancement layer andinformation indicative of the past decoded signal approximationgenerated in said second enhancement layer; wherein said informationfrom current base layer that is used to derive said second predictionincludes compression parameters employed in the base layer, and saidinformation from the first enhancement layer that is used to derive saidthird prediction includes compression parameters employed in the firstenhancement layer.
 20. The method of claim 19, wherein said compressionparameters employed in the base layer include a quantization intervalemployed in the base layer and/or said compression parameters employedin the first enhancement layer include a quantization interval employedin the first enhancement layer.
 21. A digital coding system wherein asignal is segmented into frames of data that are sequentially encoded,comprising: a base layer, said base layer including a base encoder and abase decoder; an enhancement layer, said enhancement layer including anenhancement encoder, an enhancement layer estimator, and an enhancementdecoder; said base decoder producing a reconstructed signal; saidenhancement layer estimator producing a prediction of said current frameof data; said enhancement decoder producing an enhanced reconstructedsignal; and means for predicting the current frame of data by carryingout the steps of: applying the reconstructed data representing thecurrent base layer frame and the reconstructed data representing theprevious enhancement layer frame as inputs to the enhancement layerestimator; applying additional information from the base layer asadditional input to the enhancement layer estimator; and predicting thecurrent frame of data at the enhancement layer by processing andcombining said inputs.
 22. A digital coding system wherein a signal issegmented into frames of data that are sequentially encoded, comprising:a base layer, said base layer including a base encoder and a basedecoder; an enhancement layer, said enhancement layer including anenhancement encoder, an enhancement layer estimator, and an enhancementdecoder; said base decoder producing a reconstructed signal; saidenhancement layer estimator producing a prediction of said current frameof data; said enhancement decoder producing an enhanced reconstructedsignal; and means for predicting the current frame of data by carryingout the steps of: applying the reconstructed data representing thecurrent base layer frame and the reconstructed data representing theprevious enhancement layer frame as inputs to the enhancement layerestimator; applying values of compression parameters employed by thebase layer encoder as additional inputs to the enhancement layerestimator; and predicting the current frame of data at the enhancementlayer by processing and combining said inputs.
 23. A digital codingsystem as recited in claim 22, wherein said compression parameters areparameters used for compressing the base layer prediction error.
 24. Adigital coding system as recited in claim 22, wherein said compressionparameters include a quantization interval employed in the base layercompressor.
 25. A digital coding system wherein a signal is segmentedinto frames of data that are sequentially encoded, comprising: a baselayer, said base layer including a base encoder and a base decoder; anenhancement layer, said enhancement layer including an enhancementencoder, an enhancement layer estimator, and an enhancement decoder;said base decoder producing a reconstructed signal; said enhancementlayer estimator producing a prediction of said current frame of data;said enhancement decoder producing an enhanced reconstructed signal; andmeans for predicting the current frame of data by carrying out the stepsof: applying the reconstructed data representing the current base layerframe and the reconstructed data representing the previous enhancementlayer frame as inputs to the enhancement layer estimator; applying a setof statistical parameters of the time evolution of the signal frames asinputs to the enhancement layer estimator; and predicting the currentframe of data at the enhancement layer by processing and combining saidinputs.
 26. An apparatus for scalable predictive coding of a signal,comprising: means for encoding data representing said signal with a baselayer predictive coding system that provides a first prediction of saidsignal and information indicative of a decoded base layer approximationto said signal; and means for encoding data representing said signal bya first enhancement layer which performs predictive coding with a secondprediction of said signal derived from a combination of information fromthe current base layer and information from the past decodedapproximation to said signal generated in said first enhancement layer;wherein said coding system includes a second enhancement layer andwherein said second enhancement layer performs predictive coding with athird prediction of said signal derived from a combination ofinformation from said first enhancement layer and information indicativeof the past decoded signal approximation generated in said secondenhancement layer; and wherein said information from current base layerthat is used to derive said second prediction includes compressionparameters employed in the base layer, and said information from thefirst enhancement layer that is used to derive said third predictionincludes compression parameters employed in the first enhancement layer.27. An apparatus as recited in claim 26, wherein said compressionparameters employed in the base layer include a quantization intervalemployed in the base layer and/or said compression parameters employedin the first enhancement layer include a quantization interval employedin the first enhancement layer.
 28. A digital coding system wherein asignal is segmented into frames of data that are sequentially encoded,comprising: a base layer, said base layer including a base encoder and abase decoder; an enhancement layer, said enhancement layer including anenhancement encoder, an enhancement layer estimator, and an enhancementdecoder; said base decoder producing a reconstructed signal; saidenhancement layer estimator producing a prediction of said current frameof data; said enhancement decoder producing an enhanced reconstructedsignal; a programmable data processor; and programming executable bysaid programmable data processor for predicting the current frame ofdata by carrying out the steps of: applying the reconstructed datarepresenting the current base layer frame and the reconstructed datarepresenting the previous enhancement layer frame as inputs to theenhancement layer estimator; applying additional information from thebase layer as additional input to the enhancement layer estimator; andpredicting the current frame of data at the enhancement layer byprocessing and combining said inputs.
 29. A digital coding systemwherein a signal is segmented into frames of data that are sequentiallyencoded, comprising: a base layer, said base layer including a baseencoder and a base decoder; an enhancement layer, said enhancement layerincluding an enhancement encoder, an enhancement layer estimator, and anenhancement decoder; said base decoder producing a reconstructed signal;said enhancement layer estimator producing a prediction of said currentframe of data; said enhancement decoder producing an enhancedreconstructed signal; a programmable data processor; and programmingexecutable by said programmable data processor for predicting thecurrent frame of data by carrying out the steps of: applying thereconstructed data representing the current base layer frame and thereconstructed data representing the previous enhancement layer frame asinputs to the enhancement layer estimator; applying values ofcompression parameters employed by the base layer encoder as additionalinputs to the enhancement layer estimator; and predicting the currentframe of data at the enhancement layer by processing and combining saidinputs.
 30. A digital coding system as recited in claim 29, wherein saidcompression parameters are parameters used for compressing the baselayer prediction error.
 31. A digital coding system as recited in claim29, wherein said compression parameters include a quantization intervalemployed in the base layer compressor.
 32. A digital coding systemwherein a signal is segmented into frames of data that are sequentiallyencoded, comprising: a base layer, said base layer including a baseencoder and a base decoder; an enhancement layer, said enhancement layerincluding an enhancement encoder, an enhancement layer estimator, and anenhancement decoder; said base decoder producing a reconstructed signal;said enhancement layer estimator producing a prediction of said currentframe of data; said enhancement decoder producing an enhancedreconstructed signal; and a programmable data processor; and programmingexecutable by said programmable data processor for predicting thecurrent frame of data by carrying out the steps of: applying thereconstructed data representing the current base layer frame and thereconstructed data representing the previous enhancement layer frame asinputs to the enhancement layer estimator; applying a set of statisticalparameters of the time evolution of the signal frames as inputs to theenhancement layer estimator; and predicting the current frame of data atthe enhancement layer by processing and combining said inputs.
 33. Anapparatus for scalable predictive coding of a signal, comprising: aprogrammable data processor; and programming executable by saidprogrammable data processor for carrying out the steps of: encoding datarepresenting said signal with a base layer predictive coding system thatprovides a first prediction of said signal and information indicative ofa decoded base layer approximation to said signal; and encoding datarepresenting said signal by a first enhancement layer which performspredictive coding with a second prediction of said signal derived from acombination of information from the current base layer and informationfrom the past decoded approximation to said signal generated in saidfirst enhancement layer; wherein said coding system includes a secondenhancement layer and wherein said second enhancement layer performspredictive coding with a third prediction of said signal derived from acombination of information from said first enhancement layer andinformation indicative of the past decoded signal approximationgenerated in said second enhancement layer; and wherein said informationfrom current base layer that is used to derive said second predictionincludes compression parameters employed in the base layer, and saidinformation from the first enhancement layer that is used to derive saidthird prediction includes compression parameters employed in the firstenhancement layer.
 34. An apparatus as recited in claim 33, wherein saidcompression parameters employed in the base layer include a quantizationinterval employed in the base layer and/or said compression parametersemployed in the first enhancement layer include a quantization intervalemployed in the first enhancement layer.